A Course in Data Discovery and Predictive Analytics 16 Nov 2013

A Course in Data Discovery and Predictive Analytics 16 Nov 2013 A Course in Data Discovery and Predictive Analytics David M. Levine, Baruch College CUNY Kathryn A. Szabat, La Salle University David F. Stephan, Two Bridges Instructional Technology analytics.davidlevinestatistics.com DSI MSMESB session, November 16, 2013 What Are We Talking About? A definition of business analytics Broad categories of business analytics (INFORMS 2010-2011) Business analytics continues to become increasingly important in business and therefore in business education 1 2 Course Justification and Starting Points Addresses a topic of growing interest Introduces methods of problem description and decision-making not seen elsewhere in the business statistics curriculum Assumes a pre-requisite introductory course that covers descriptive statistics, confidence intervals and hypothesis testing, and simple linear regression Presents methods that have antecedents in introductory course Guiding Principles Technology use should not hamper students ability to learn concepts Emphasize application of methods (business students are the audience) Compare and contrast with decision-making using traditional methods where possible. Capitalize on insights gained teaching related subjects such as CIS and OR/MS 3 4 How Our Teaching Experience Informs Us As a team, our varied backgrounds and interests contribute to shaping our choices How David Levine s Teaching Experience Informs Us Have sought to make statistics useful to students majoring in the functional areas of accounting, economics/finance, management, and marketing Have changed my focus as changes in technology occurred over time 5 6 2013 Levine Szabat Stephan DSI MEMESB slides.pdf 1

A Course in Data Discovery and Predictive Analytics 16 Nov 2013 Early 1980s Integrated software such as SAS, SPSS, and Minitab into introductory course Enabled me to begin focusing on results rather than calculations Helped me realize that students trained to use statistical programs would have increased opportunities in business Late 1980s/early 1990s Started to focus on software with enhanced user interfaces that replaced older, programmingoriented interfaces Saw how this would make statistical tools more accessible to novice students, in particular. 7 8 Early 1990s Integrated Deming s Total Quality Management philosophy and practices into the introductory course. Through consulting work, learned the importance of organizational culture and the difficulty of implementing change This had limited long term impact as coverage of this topic migrated to operations management Late 1990s Pondered the use of Microsoft Excel, by then prevalent in business schools Realized Excel needed to be modified for classroom use Crossed paths and discovered shared interests with David Stephan 9 10 Current Day Reflected on analytics Crossed path and discovered shared interests with Kathy Szabat. Realized this is our best opportunity to make business statistics critical to the success of majors in the functional areas Believe this represents an opportunity to develop new majors in analytics and revise majors in business statistics (CIS, et. al.) Kathryn Szabat s Experience Overarching guiding principle: Statistics plays a role in problem solving and decision making. Statistics the methods that help transform data into useful information for decision makers Provides support for gut feeling, intuition, experience Provides opportunity to gain insight 11 12 2013 Levine Szabat Stephan DSI MEMESB slides.pdf 2

A Course in Data Discovery and Predictive Analytics 16 Nov 2013 Have consistently emphasized applications of statistics to functional areas of business Continual outreach to colleagues in different departments within the school of business to better understand how statistics is used in the various functional areas Have used technology extensively in the course Without compromising understanding of logic of formulas Advocating the importance of using a tool to generate results 13 14 Have increased, over time, focus on problemsolving and decisionmaking With attention to formulating the problem Have increased, over time, focus on interpretation and communication Someone has to tell the story at the end 15 16 Have recently been engaged in developing a new, interdisciplinary academic department, Business Systems and Analytics Effort as a response to the technology and datadriven changes in business today Outreach to practitioners to better understand business analytics as an emerging field Developed an introductory presentation on business analytics to be used by all faculty in the introductory statistics course (as well as introductory IS and operations courses) David Stephan s Experience Visualization has always been a theme in my work and interests Context-based learning advocate Witnessed and taught about several generations of information technology 17 18 2013 Levine Szabat Stephan DSI MEMESB slides.pdf 3

A Course in Data Discovery and Predictive Analytics 16 Nov 2013 How things work versus how to work with things Do you remember the ALU and CU? CP/M or DOS Which is the better choice? When is the last time someone asked you about the ASCII table? Relational Database Debate The story of the textbook that omitted the dbase language Accept Last Name: to lastname Input Grade: to grade @5,10 SAY Trim(lastname) + grade PICTURE 99.9 Should database examples use one relation or two or more? 19 20 Lessons from the Debate Simpler things can be used to teach operating principles and simulate more complex things Large-scale things can be imagined from smallscale things Don t fuss over technology choices in the long-run, your choice will most likely not be future-proof! Challenge: Finding the right level of abstraction to teach. If you don t teach {formulas, computations, fully explain methods, widgets, whatever}, students will not understand anything. How many helpful black boxes do you already use without explanation? The Microsoft Excel xls file format Don t try to reveal/decompose all complex systems Can end up discussing parts that, at a later time, get use as an integrated whole 21 22 New Challenges to Address Volume, velocity, and variety How to address these data characteristics often associated with analytics? Semi-subjective analysis of outputs (e.g., 3D scatterplots or cluster plots) Examining patterns before testing hypotheses Need to determine when to assign causality (to relationships) as part of the analysis versus testing a hypothesized causality Seeking Course Bests Best Topics to Teach Best Technology to Use Best Context to Deliver Instruction 23 24 2013 Levine Szabat Stephan DSI MEMESB slides.pdf 4

A Course in Data Discovery and Predictive Analytics 16 Nov 2013 Best Topics to Teach Descriptive analytics/data discovery: most likely to be seen, builds on and extends introductory descriptive methods. Can be used to raise and simulate volume and velocity issues. Predictive not prescriptive analytics. The latter brings into play management insight, judgment, and wisdom. (Predictive combines traditional statistical analysis with data mining, as defined earlier.) Best Technology to Use Experience teaches us not to be overly concerned about choice! No one program, application, or package is best in 2013 Best technology combines most accessible with what bests illustrates the concept Our choice: mix of Microsoft Excel, Tableau Public, and JMP 25 26 Best Context to Deliver Instruction A broad case that represents an enterprise of suitable complexity, yet one that can be understandable on a casual level Our choice: a theme park with several different parts ( lands ) and an integrated resort hotel Course Description In-Depth 27 28 Introduction (2) Descriptive Analytics (2) Topic List (with suggested weeks) Preparing for Predictive Analytics (1) Multiple regression including residual analysis, dummy variables, interaction terms, and influence analysis (1.5-2) Logistic regression (1) Multiple regression model building including transformations, collinearity, stepwise regression, and best subsets (1.5-2) Predictive Analytics (4-5) Introduction (2 weeks) How We Got Here: Evolutionary changes that have led to more widespread usage of analytics How analytics can change the data analysis and decision-making processes Basic vocabulary and taxonomy of analytics Technology requirements and orientation 29 30 2013 Levine Szabat Stephan DSI MEMESB slides.pdf 5

A Course in Data Discovery and Predictive Analytics 16 Nov 2013 Provide information about the current status of a business or business activity in a form easy to comprehend and review. Descriptive Analytics (2 weeks) Summarizing volume and velocity Sexiness versus usefulness issue Levels of summary: drill down, levels of hierarchy, and subsetting Information design principles that inform descriptive methods Summarizing volume and velocity: Dashboards 31 32 Sexiness versus usefulness: Gauges vs. bullet graphs Example: combining a numerical measure with a categorical group Which one looks more sexy, appealing, interesting, etc.? Which one best facilitates comparisons? What if the answers to the two questions are different? Sexiness versus usefulness: Gauges vs. bullet graphs 33 34 Sexiness versus usefulness: Gauges vs. bullet graphs Which one looks more sexy, appealing, interesting, etc.? Which one best facilitates comparisons? What if the answers to the two questions are different? Levels of summary: drill down, levels of hierarchy, and subsetting Drill-down sequence example (using Excel) 35 36 2013 Levine Szabat Stephan DSI MEMESB slides.pdf 6

A Course in Data Discovery and Predictive Analytics 16 Nov 2013 Financial example showing another level of drill-down Visual drill-down using a tree map Levels of summary: drill down, levels of hierarchy, and subsetting Levels of summary: drill down, levels of hierarchy, and subsetting 37 38 Subsetting using slicers (Excel) Levels of summary: drill down, levels of hierarchy, and subsetting Information design principles Fostering efficient and effective communication and understanding Provide context for data in a compact presentation Add additional dimensions of data Misuse raises issues beyond typical statistical concerns: visual perception, artistic considerations 39 40 Does this tree map provide context for data in a compact presentation? Add additional dimensions of data? Tree Map of Retirement Fund Assets Colored by 10-Year Return Percentage, By Fund Type (JMP) Does this table provide context for data in a compact presentation? Sparklines example (Excel) GROWTH FUNDS VALUE FUNDS 41 42 2013 Levine Szabat Stephan DSI MEMESB slides.pdf 7

A Course in Data Discovery and Predictive Analytics 16 Nov 2013 Tree Map of Number of Social Media Comments Colored by Tone, By Land (Excel) Nobel Laureates Graph (Accurat information design agency) Information design tree map example with simpler data Information design principles: infographics 43 44 Detail of Nobel Prize Laureates Graph Information design principles: infographics Preparing for Predictive Analytics (1 week) Confidence intervals Hypothesis testing Simple linear regression 45 46 Confidence intervals Normal distribution Sampling distributions Confidence intervals for the mean and proportion Hypothesis testing Basic Concepts of hypothesis testing p-values Tests for the differences between means and proportions 47 48 2013 Levine Szabat Stephan DSI MEMESB slides.pdf 8

A Course in Data Discovery and Predictive Analytics 16 Nov 2013 Simple linear regression The simple linear regression model Interpreting the regression coefficients Residual analysis Assumptions of regression Inferences in simple linear regression Multiple Regression (1.5-2 weeks) Developing the multiple regression model Inference in multiple regression Residual analysis Dummy variables Interaction terms Influence analysis 49 50 Developing the multiple regression model Interpreting the coefficients Coefficients of multiple determination Coefficients of partial determination Assumptions Inference in multiple regression Testing the overall model Testing the contribution of each independent variable Adjusted r 2 51 52 Residual analysis Plots of the residuals vs. independent variables Plots of the residuals vs. predicted Y Plots of the residuals vs. time (if appropriate) Dummy variables Using categorical independent variables in a regression model: Defining dummy variables Interpreting dummy variables Assumptions in using dummy variables 53 54 2013 Levine Szabat Stephan DSI MEMESB slides.pdf 9

A Course in Data Discovery and Predictive Analytics 16 Nov 2013 Interaction terms What they are Why they are sometimes necessary Interpreting interaction terms Influence analysis Examining the effect of individual observations on the regression model Hat matrix elements h i Studentized deleted residuals t i Cook s Distance statistic D i 55 56 Predicting the likelihood of upgrading to a premium credit card based on the monthly purchase amount and whether the account has multiple cards Logistic regression (1 week) Predicting a categorical dependent variable Cannot use least squares regression Odds ratio Logistic regression model Predicting probability of an event of interest Deviance statistic Wald statistic Logistic regression example using an Excel add-in 57 58 Multiple Regression Model Building (1.5-2 weeks) Transformations Collinearity Stepwise regression Best subsets regression Transformations Purposes Square root transformations Logarithmic transformations 59 60 2013 Levine Szabat Stephan DSI MEMESB slides.pdf 10

A Course in Data Discovery and Predictive Analytics 16 Nov 2013 Collinearity Effect on the regression model Measuring the variance inflationary factor (VIF) Dealing with collinear independent variables Stepwise regression History How it works Limitations Use in an era of big data 61 62 METHOD FOR METHOD Prediction Classification Clustering Association Best subsets regression How it works Advantages and disadvantages vs. stepwise regression Mallows C p statistic Predictive Analytics (4-5 weeks) Classification and regression trees (1-1.5 weeks) Neural networks (1-1.5 weeks) Cluster analysis (1 week) Multidimensional scaling (1week) 63 64 Classification and regression trees Decision trees that split data into groups based on the values of independent or explanatory (X) variables. Not affected by the distribution of the variables Splitting determines which values of a specific independent variable are useful in predicting the dependent (Y) variable present Using a categorical dependent Y variable results in a classification tree Using a numerical dependent Y variable results in a regression tree Rules for splitting the tree Pruning back a tree If possible, divide data into training sample and validation sample Classification tree example Predicting the likelihood of upgrading to a premium credit card based on the monthly purchase amount and whether the account has multiple cards (same example used in logistic regression) 65 66 2013 Levine Szabat Stephan DSI MEMESB slides.pdf 11

A Course in Data Discovery and Predictive Analytics 16 Nov 2013 Predicting the likelihood of upgrading to a premium credit card based on the monthly purchase amount and whether the account has multiple cards (same example used in logistic regression) Predicting sales of energy bars based on price and promotion expenses (could be multiple regression example, too) Classification tree example Regression tree example 67 68 Neural nets Constructs models from patterns and relationships uncovered in data Computations that begin with inputs and end with outputs Uses a hyperbolic tangent function Divide data into training sample and validation sample Neural net example 1 Predicting the likelihood of upgrading to a premium credit card based on the monthly purchase amount and whether the account has multiple cards (same example used for logistic regression and classification tree) 69 70 Neural net example 2 Predicting sales of energy bars based on price and promotion expenses (same example used in regression tree) Cluster analysis Classifies data into a sequence of groupings such that objects in each group are more alike other objects in their group than they are to objects found in other groups. Hierarchical clustering k-means clustering Distance measures Types of linkage between clusters 71 72 2013 Levine Szabat Stephan DSI MEMESB slides.pdf 12

A Course in Data Discovery and Predictive Analytics 16 Nov 2013 Perception of sports based on a survey of these attributes: movement speed, rules, team orientation, amount of contact Cluster analysis example Multidimensional scaling Visualizes objects in a two or more dimensional space, or map, with the goal of discovering patterns of similarities or dissimilarities among the objects. Types of multidimensional scaling Distance measures Stress statistic measure of fit Challenge in interpreting dimensions 73 74 Perception of sports based on a survey of these attributes: movement speed, rules, team orientation, amount of contact Perception of sports based on a survey of these attributes: movement speed, rules, team orientation, amount of contact Multidimensional scaling example using JMP add-in Multidimensional scaling example using JMP add-in 75 76 Software Resources Microsoft Excel (latest versions equipped Apps for Office) Good for selected dashboard elements (treemap, gauges, sparklines) and illustrating drill-down (with PivotTables) and subsetting (with Slicers) Extend with third-party add-ins to perform logistic regression Tableau Public (web-based, free download) Good for descriptive analytics (bullet graph, treemaps) Drag-and-drop interface that can be taught in minutes Premium version (not free) extends utility of software to many other methods, although this server-based version is more geared to business JMP Many displays have drill-down built into them Good for regression trees, neural nets, cluster analysis, and multidimensional scaling (with additional free add-in) Requires SAS or R for some processing; user interface contains some quirks for new and casual users (most of which could be eliminated through the use of custom add-ins) Future versions promise additional capabilities. Can I Incorporate Any of This Into the Introductory Course? Could add some of the descriptive analytics into the introductory course Drill down and subsetting Perhaps one graph that summarize volume and velocity Show-and-tell to illustrate information design and/or sexiness versus usefulness issue Could add binary logistic regression if your course covers multiple regression and mentions binary logistic regression, but this will not be feasible in most cases Funny, you should ask that question. 77 78 2013 Levine Szabat Stephan DSI MEMESB slides.pdf 13

A Course in Data Discovery and Predictive Analytics 16 Nov 2013 References Berenson, M. L., D. M. Levine, and K. A. Szabat. Basic Business Statistics 13th edition. Upper Saddle River: Pearson Education, forthcoming January 2014. Breiman, L., J. Friedman, C. J. Stone, and R. A. Olshen. Classification and Regression Trees. London: Chapman and Hall, 1984. Cox, T. F., and M. A. Cox. Multidimensional Scaling, Second edition. Boca Raton, FL: CRC Press, 2010. Everitt, B. S., S. Landau, and M. Leese. Cluster Analysis, Fifth edition. New York: John Wiley, 2011. Few, S. Information Dashboard Design: Displaying Data for At-a-Glance Monitoring, Second edition. Burlingame, CA: Analytics Press, 2013. Hakimpoor, H., K. Arshad, H. Tat, N. Khani, and M. Rahmandoust. Artificial Neural Network Application in Management. World Applied Sciences Journal, 2011, 14(7): 1008 1019. R. Klimberg, and B. D. McCullough. Fundamentals of Predictive Analytics with JMP. Cary, NC: SAS Press. 2013 Lindoff, G., and M. Berry. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Hoboken, NJ: Wiley Publishing, Inc., 2011. Loh, W. Y. Fifty years of classification and regression trees. International Statistical Review, 2013, in press Tufte, E. Beautiful Evidence. Cheshire, CT: Graphics Press, 2006. Further Information or Contact Contact us at analytics@davidlevinestatistics.com Visit analytics.davidlevinestatistics.com for Today s slides including references A preview of some of our current work in this area Coming soon WaldoLands.com Look for our (very occasional) tweets using #AnalyticsEducation 79 80 2013 Levine Szabat Stephan DSI MEMESB slides.pdf 14

A Course in Data Discovery and Predictive Analytics David M. Levine, Baruch College CUNY Kathryn A. Szabat, La Salle University David F. Stephan, Two Bridges Instructional Technology analytics.davidlevinestatistics.com DSI MSMESB session, November 16, 2013 16 Nov 2013 2013 Levine Szabat Stephan DSI MEMESB slides.pdf 1

What Are We Talking About? A definition of business analytics Broad categories of business analytics (INFORMS 2010-2011) Business analytics continues to become increasingly important in business and therefore in business education

Course Justification and Starting Points Addresses a topic of growing interest Introduces methods of problem description and decision-making not seen elsewhere in the business statistics curriculum Assumes a pre-requisite introductory course that covers descriptive statistics, confidence intervals and hypothesis testing, and simple linear regression Presents methods that have antecedents in introductory course

Guiding Principles Technology use should not hamper students ability to learn concepts Emphasize application of methods (business students are the audience) Compare and contrast with decision-making using traditional methods where possible. Capitalize on insights gained teaching related subjects such as CIS and OR/MS

How Our Teaching Experience Informs Us As a team, our varied backgrounds and interests contribute to shaping our choices

How David Levine s Teaching Experience Informs Us Have sought to make statistics useful to students majoring in the functional areas of accounting, economics/finance, management, and marketing Have changed my focus as changes in technology occurred over time

Early 1980s Integrated software such as SAS, SPSS, and Minitab into introductory course Enabled me to begin focusing on results rather than calculations Helped me realize that students trained to use statistical programs would have increased opportunities in business

Late 1980s/early 1990s Started to focus on software with enhanced user interfaces that replaced older, programmingoriented interfaces Saw how this would make statistical tools more accessible to novice students, in particular.

Early 1990s Integrated Deming s Total Quality Management philosophy and practices into the introductory course. Through consulting work, learned the importance of organizational culture and the difficulty of implementing change This had limited long term impact as coverage of this topic migrated to operations management

Late 1990s Pondered the use of Microsoft Excel, by then prevalent in business schools Realized Excel needed to be modified for classroom use Crossed paths and discovered shared interests with David Stephan

Current Day Reflected on analytics Crossed path and discovered shared interests with Kathy Szabat. Realized this is our best opportunity to make business statistics critical to the success of majors in the functional areas Believe this represents an opportunity to develop new majors in analytics and revise majors in business statistics (CIS, et. al.)

Kathryn Szabat s Experience Overarching guiding principle: Statistics plays a role in problem solving and decision making. Statistics the methods that help transform data into useful information for decision makers Provides support for gut feeling, intuition, experience Provides opportunity to gain insight

Have consistently emphasized applications of statistics to functional areas of business Continual outreach to colleagues in different departments within the school of business to better understand how statistics is used in the various functional areas

Have used technology extensively in the course Without compromising understanding of logic of formulas Advocating the importance of using a tool to generate results

Have increased, over time, focus on problemsolving and decisionmaking With attention to formulating the problem

Have increased, over time, focus on interpretation and communication Someone has to tell the story at the end

Have recently been engaged in developing a new, interdisciplinary academic department, Business Systems and Analytics Effort as a response to the technology and datadriven changes in business today Outreach to practitioners to better understand business analytics as an emerging field Developed an introductory presentation on business analytics to be used by all faculty in the introductory statistics course (as well as introductory IS and operations courses)

David Stephan s Experience Visualization has always been a theme in my work and interests Context-based learning advocate Witnessed and taught about several generations of information technology

How things work versus how to work with things Do you remember the ALU and CU? CP/M or DOS Which is the better choice? When is the last time someone asked you about the ASCII table?

Relational Database Debate The story of the textbook that omitted the dbase language Accept Last Name: to lastname Input Grade: to grade @5,10 SAY Trim(lastname) + grade PICTURE 99.9 Should database examples use one relation or two or more?

Lessons from the Debate Simpler things can be used to teach operating principles and simulate more complex things Large-scale things can be imagined from smallscale things Don t fuss over technology choices in the long-run, your choice will most likely not be future-proof!

Challenge: Finding the right level of abstraction to teach. If you don t teach {formulas, computations, fully explain methods, widgets, whatever}, students will not understand anything. How many helpful black boxes do you already use without explanation? The Microsoft Excel xls file format Don t try to reveal/decompose all complex systems Can end up discussing parts that, at a later time, get use as an integrated whole

New Challenges to Address Volume, velocity, and variety How to address these data characteristics often associated with analytics? Semi-subjective analysis of outputs (e.g., 3D scatterplots or cluster plots) Examining patterns before testing hypotheses Need to determine when to assign causality (to relationships) as part of the analysis versus testing a hypothesized causality

Seeking Course Bests Best Topics to Teach Best Technology to Use Best Context to Deliver Instruction

Best Topics to Teach Descriptive analytics/data discovery: most likely to be seen, builds on and extends introductory descriptive methods. Can be used to raise and simulate volume and velocity issues. Predictive not prescriptive analytics. The latter brings into play management insight, judgment, and wisdom. (Predictive combines traditional statistical analysis with data mining, as defined earlier.)

Best Technology to Use Experience teaches us not to be overly concerned about choice! No one program, application, or package is best in 2013 Best technology combines most accessible with what bests illustrates the concept Our choice: mix of Microsoft Excel, Tableau Public, and JMP

Best Context to Deliver Instruction A broad case that represents an enterprise of suitable complexity, yet one that can be understandable on a casual level Our choice: a theme park with several different parts ( lands ) and an integrated resort hotel

Course Description In-Depth

Topic List (with suggested weeks) Introduction (2) Descriptive Analytics (2) Preparing for Predictive Analytics (1) Multiple regression including residual analysis, dummy variables, interaction terms, and influence analysis (1.5-2) Logistic regression (1) Multiple regression model building including transformations, collinearity, stepwise regression, and best subsets (1.5-2) Predictive Analytics (4-5)

Introduction (2 weeks) How We Got Here: Evolutionary changes that have led to more widespread usage of analytics How analytics can change the data analysis and decision-making processes Basic vocabulary and taxonomy of analytics Technology requirements and orientation

Descriptive Analytics (2 weeks) Summarizing volume and velocity Sexiness versus usefulness issue Levels of summary: drill down, levels of hierarchy, and subsetting Information design principles that inform descriptive methods

Summarizing volume and velocity: Dashboards Provide information about the current status of a business or business activity in a form easy to comprehend and review.

Sexiness versus usefulness: Gauges vs. bullet graphs Example: combining a numerical measure with a categorical group Which one looks more sexy, appealing, interesting, etc.? Which one best facilitates comparisons? What if the answers to the two questions are different?

Sexiness versus usefulness: Gauges vs. bullet graphs

Sexiness versus usefulness: Gauges vs. bullet graphs Which one looks more sexy, appealing, interesting, etc.? Which one best facilitates comparisons? What if the answers to the two questions are different?

Levels of summary: drill down, levels of hierarchy, and subsetting Drill-down sequence example (using Excel)

Levels of summary: drill down, levels of hierarchy, and subsetting Financial example showing another level of drill-down

Levels of summary: drill down, levels of hierarchy, and subsetting Visual drill-down using a tree map

Levels of summary: drill down, levels of hierarchy, and subsetting Subsetting using slicers (Excel)

Information design principles Fostering efficient and effective communication and understanding Provide context for data in a compact presentation Add additional dimensions of data Misuse raises issues beyond typical statistical concerns: visual perception, artistic considerations

Does this tree map provide context for data in a compact presentation? Tree Map of Retirement Fund Assets Colored by 10-Year Return Percentage, By Fund Type (JMP) Add additional dimensions of data? GROWTH FUNDS VALUE FUNDS

Does this table provide context for data in a compact presentation? Sparklines example (Excel)

Information design tree map example with simpler data Tree Map of Number of Social Media Comments Colored by Tone, By Land (Excel)

Information design principles: infographics Nobel Laureates Graph (Accurat information design agency)

Information design principles: infographics Detail of Nobel Prize Laureates Graph

Preparing for Predictive Analytics (1 week) Confidence intervals Hypothesis testing Simple linear regression

Confidence intervals Normal distribution Sampling distributions Confidence intervals for the mean and proportion

Hypothesis testing Basic Concepts of hypothesis testing p-values Tests for the differences between means and proportions

Simple linear regression The simple linear regression model Interpreting the regression coefficients Residual analysis Assumptions of regression Inferences in simple linear regression

Multiple Regression (1.5-2 weeks) Developing the multiple regression model Inference in multiple regression Residual analysis Dummy variables Interaction terms Influence analysis

Developing the multiple regression model Interpreting the coefficients Coefficients of multiple determination Coefficients of partial determination Assumptions

Inference in multiple regression Testing the overall model Testing the contribution of each independent variable Adjusted r 2

Residual analysis Plots of the residuals vs. independent variables Plots of the residuals vs. predicted Y Plots of the residuals vs. time (if appropriate)

Dummy variables Using categorical independent variables in a regression model: Defining dummy variables Interpreting dummy variables Assumptions in using dummy variables

Interaction terms What they are Why they are sometimes necessary Interpreting interaction terms

Influence analysis Examining the effect of individual observations on the regression model Hat matrix elements h i Studentized deleted residuals t i Cook s Distance statistic D i

Logistic regression (1 week) Predicting a categorical dependent variable Cannot use least squares regression Odds ratio Logistic regression model Predicting probability of an event of interest Deviance statistic Wald statistic

Logistic regression example using an Excel add-in Predicting the likelihood of upgrading to a premium credit card based on the monthly purchase amount and whether the account has multiple cards

Multiple Regression Model Building (1.5-2 weeks) Transformations Collinearity Stepwise regression Best subsets regression

Transformations Purposes Square root transformations Logarithmic transformations

Collinearity Effect on the regression model Measuring the variance inflationary factor (VIF) Dealing with collinear independent variables

Stepwise regression History How it works Limitations Use in an era of big data

Best subsets regression How it works Advantages and disadvantages vs. stepwise regression Mallows C p statistic

Predictive Analytics (4-5 weeks) METHOD FOR METHOD Prediction Classification Clustering Association Classification and regression trees (1-1.5 weeks) Neural networks (1-1.5 weeks) Cluster analysis (1 week) Multidimensional scaling (1week)

Classification and regression trees Decision trees that split data into groups based on the values of independent or explanatory (X) variables. Not affected by the distribution of the variables Splitting determines which values of a specific independent variable are useful in predicting the dependent (Y) variable present Using a categorical dependent Y variable results in a classification tree Using a numerical dependent Y variable results in a regression tree Rules for splitting the tree Pruning back a tree If possible, divide data into training sample and validation sample

Classification tree example Predicting the likelihood of upgrading to a premium credit card based on the monthly purchase amount and whether the account has multiple cards (same example used in logistic regression)

Classification tree example Predicting the likelihood of upgrading to a premium credit card based on the monthly purchase amount and whether the account has multiple cards (same example used in logistic regression)

Regression tree example Predicting sales of energy bars based on price and promotion expenses (could be multiple regression example, too)

Neural nets Constructs models from patterns and relationships uncovered in data Computations that begin with inputs and end with outputs Uses a hyperbolic tangent function Divide data into training sample and validation sample

Neural net example 1 Predicting the likelihood of upgrading to a premium credit card based on the monthly purchase amount and whether the account has multiple cards (same example used for logistic regression and classification tree)

Neural net example 2 Predicting sales of energy bars based on price and promotion expenses (same example used in regression tree)

Cluster analysis Classifies data into a sequence of groupings such that objects in each group are more alike other objects in their group than they are to objects found in other groups. Hierarchical clustering k-means clustering Distance measures Types of linkage between clusters

Cluster analysis example Perception of sports based on a survey of these attributes: movement speed, rules, team orientation, amount of contact

Multidimensional scaling Visualizes objects in a two or more dimensional space, or map, with the goal of discovering patterns of similarities or dissimilarities among the objects. Types of multidimensional scaling Distance measures Stress statistic measure of fit Challenge in interpreting dimensions

Multidimensional scaling example using JMP add-in Perception of sports based on a survey of these attributes: movement speed, rules, team orientation, amount of contact

Multidimensional scaling example using JMP add-in Perception of sports based on a survey of these attributes: movement speed, rules, team orientation, amount of contact

Microsoft Excel (latest versions equipped Apps for Office) Good for selected dashboard elements (treemap, gauges, sparklines) and illustrating drill-down (with PivotTables) and subsetting (with Slicers) Extend with third-party add-ins to perform logistic regression Software Resources Tableau Public (web-based, free download) Good for descriptive analytics (bullet graph, treemaps) Drag-and-drop interface that can be taught in minutes Premium version (not free) extends utility of software to many other methods, although this server-based version is more geared to business JMP Many displays have drill-down built into them Good for regression trees, neural nets, cluster analysis, and multidimensional scaling (with additional free add-in) Requires SAS or R for some processing; user interface contains some quirks for new and casual users (most of which could be eliminated through the use of custom add-ins) Future versions promise additional capabilities.

Can I Incorporate Any of This Into the Introductory Course? Could add some of the descriptive analytics into the introductory course Drill down and subsetting Perhaps one graph that summarize volume and velocity Show-and-tell to illustrate information design and/or sexiness versus usefulness issue Could add binary logistic regression if your course covers multiple regression and mentions binary logistic regression, but this will not be feasible in most cases Funny, you should ask that question.

References Berenson, M. L., D. M. Levine, and K. A. Szabat. Basic Business Statistics 13th edition. Upper Saddle River: Pearson Education, forthcoming January 2014. Breiman, L., J. Friedman, C. J. Stone, and R. A. Olshen. Classification and Regression Trees. London: Chapman and Hall, 1984. Cox, T. F., and M. A. Cox. Multidimensional Scaling, Second edition. Boca Raton, FL: CRC Press, 2010. Everitt, B. S., S. Landau, and M. Leese. Cluster Analysis, Fifth edition. New York: John Wiley, 2011. Few, S. Information Dashboard Design: Displaying Data for At-a-Glance Monitoring, Second edition. Burlingame, CA: Analytics Press, 2013. Hakimpoor, H., K. Arshad, H. Tat, N. Khani, and M. Rahmandoust. Artificial Neural Network Application in Management. World Applied Sciences Journal, 2011, 14(7): 1008 1019. R. Klimberg, and B. D. McCullough. Fundamentals of Predictive Analytics with JMP. Cary, NC: SAS Press. 2013 Lindoff, G., and M. Berry. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Hoboken, NJ: Wiley Publishing, Inc., 2011. Loh, W. Y. Fifty years of classification and regression trees. International Statistical Review, 2013, in press Tufte, E. Beautiful Evidence. Cheshire, CT: Graphics Press, 2006.

Further Information or Contact Contact us at analytics@davidlevinestatistics.com Visit analytics.davidlevinestatistics.com for Today s slides including references A preview of some of our current work in this area Coming soon WaldoLands.com Look for our (very occasional) tweets using #AnalyticsEducation